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处理是否有益？有序结局因果推断的贝叶斯方法AI 解读

Randomized experiments with ordinal outcomes are common across scientific applications, but conventional causal estimands such as the average treatment effect are difficult to interpret because ordinal categories lack meaningful numerical spacing. Two interpretable estimands, the probabilities that the treatment is beneficial and strictly beneficial, depend on the joint distribution of the potential outcomes and are therefore not identifiable from observed data. The sharp…

多维聚类依赖下无需交叉拟合的去偏机器学习AI 解读

This paper develops an asymptotic theory for two-step debiased machine learning (DML) estimators in generalised method of moments (GMM) models with general multiway clustered dependence, without relying on cross-fitting. While cross-fitting is commonly employed, it can be statistically inefficient and computationally burdensome when first-stage learners are complex and the effective sample size is governed by the number of independent clusters. We show that valid inference…

图分割贝叶斯因果森林：空间异质性处理效应估计AI 解读

In spatial observational studies, treatment assignment and outcomes often exhibit spatial dependence patterns, and treatment effects may vary across space and subpopulations due to both measured and unmeasured spatially structured confounders. Accounting for spatial dependence while estimating heterogeneous treatment effects (HTEs) is a central task in spatial causal inference. Causal Bayesian additive regression tree methods are popular nonparametric methods for modeling…

潜在结果变量因果效应的非参数识别与估计AI 解读

How should researchers conduct causal inference when the outcome of interest is latent and measured imperfectly by multiple indicators? We develop a general nonparametric framework for identifying and estimating average treatment effects on latent outcomes in randomized experiments. We show that latent-outcome estimation faces a fundamental but overlooked noncomparability problem due to measurement-set dependence: adding, removing, or replacing measurement targets a…

大面板中的简洁、无需特定参数设定的因果推断AI 解读

We propose a simple and transparent approach to causal inference using panel data with a large number of units and periods. Instead of imposing a particular parametric specification on the untreated potential outcomes, we assume they follow a nonparametric nonseparable factor model. We use long pre-treatment outcome histories to construct a measure of similarity between the latent factors of different units. Using this measure, we impute missing counterfactual means and…

BGM-IV：面向高维协变量工具变量回归的贝叶斯生成建模AI 解读

Instrumental-variable (IV) regression enables causal estimation under endogeneity, but modern IV problems often involve nonlinear structural effects and high-dimensional covariates. Existing methods typically operate in observed or generic learned feature spaces, and they often yield point estimates without uncertainty quantification. We introduce BGM-IV, a Bayesian generative modeling approach that performs nonlinear IV regression through posterior inference in a causally…

离散时间滞后鞅差阵列的稳定中心极限定理及动态因果应用AI 解读

Recent work in dynamic causal inference introduced a class of discrete-time stochastic processes that generalize martingale difference sequences and arrays as follows: the random variates in each sequence have expectation zero given certain lagged filtrations but not given the natural filtration. We formalize this class of stochastic processes and prove stable central limit theorems (CLTs) via martingale-coboundary decomposition, leveraging the classical martingale CLT. We…

双重差分与合成控制的结合：双重稳健识别和估计AI 解读

Difference-in-Differences (DiD) and Synthetic Control (SC) are widely used methods for causal inference in panel data, each with distinct strengths and limitations. We propose a novel method for short-panel causal inference that integrates the advantages of both approaches. Our method delivers a doubly robust identification strategy for the average treatment effect on the treated (ATT) under either of two non-nested assumptions: parallel trends or a group-level SC condition.…

结合实验与观察数据对分布处理效应参数的识别能力AI 解读

This study investigates whether and how combining experimental data, in which treatment is randomized, with observational data, in which treatment is self-selected, improves identification of distributional treatment effect (DTE) parameters. Many DTE parameters remain partially identified even under randomized treatment assignment. We show that the combined data reveal how potential-outcome distributions vary across latent self-selection types and that this information fully…

非线性政治面板中的预测必要性与因果发现：民主化的反馈和动态AI 解读

A non-significant coefficient in a dynamic panel model need not imply the absence of a relationship. It may instead reflect heterogeneous effects averaged toward zero, reciprocal dynamics overlooked by a recursive specification, or relationships masked by the omission of correlated covariates. Standard linear estimators cannot distinguish among these possibilities. We develop an inferential workflow for political panel data that resolves this ambiguity by combining flexible…

含时变中介变量与滞后结果的双重差分模型AI 解读

We study difference-in-differences (DiD) designs in which a binary treatment changes an endogenous time-varying (continuous, discrete, or mixed) mediator that in turn affects an outcome. We allow for the inclusion of lagged outcomes in the model. Under our model assumptions, we show that the usual DiD estimand mixes the average direct effect on the treated, the average indirect effect, and a trend bias term. A two-way fixed effects (TWFE) regression that controls for the…

整群随机试验中依从效应与网络因果效应的识别和有效估计AI 解读

Treatment noncompliance is pervasive in infectious disease cluster-randomized trials. Although all individuals within a cluster are assigned the same treatment condition, the treatment uptake status may vary across individuals due to noncompliance. We propose a semiparametric framework to evaluate the individual compliance effect and network assignment effect within principal stratum exhibiting different patterns of noncompliance. The individual compliance effect captures…

存在干扰时的再随机化AI 解读

Covariates are widely used in randomized experiments to improve precision. However, in the presence of interference, where outcomes may depend on the treatment assignments of other units, standard covariate adjustment methods may fail to preserve desirable properties such as the no-harm property, meaning that incorporating covariates does not worsen estimator performance. Existing approaches that incorporate covariates under interference typically rely on specifying a…

逐步放量即可：网络干扰下 A/B 测试回归估计量的去偏AI 解读

Network interference complicates A/B testing on online platforms, such as social networks and marketplaces, where causal methods based on single experiments often suffer from significant bias due to complex interference patterns. This paper demonstrates the statistical benefits of merging data from multiple experiments with varying treatment proportions. Sequential experimentation with increasing traffic, or ramp-up, is widely used in tech companies for risk management and…

切片 Lp 分布平衡方法AI 解读

A popular class of causal inference methods addresses confounding through weighting, which reweights treated and control groups to balance their covariate distributions without using outcome information, thereby preserving a design-based perspective. In this paper, we propose sliced $L^p$ distributional balancing (SLDB), a family indexed by $p\in[1,\infty)$ that measures imbalance by averaging squared $L^p$ distances between the cumulative distribution functions of…

表格基础模型能否摊销统计推断成本？AI 解读

For decades, statistical inference has largely been developed one problem at a time. Given a scientific target, such as a treatment effect or a regression function, statisticians design a problem-specific estimator together with a procedure for quantifying its uncertainty. This paper proposes a different paradigm. We focus on a classical problem in statistical inference, confidence interval construction, and develop TabCon, an amortized inference system built on a tabular…

线性模型中的最优检验及其因果推断含义AI 解读

Despite its widespread use in practice, multiple regression suffers from two important limitations due to ignoring the structure between predictors. First, reported coefficients are, in fact, direct effects in a causal context. These are often less meaningful than total effects when accounting for a factor's contribution, however, estimating the latter typically requires a causal diagram to be specified among predictors. The second limitation is multicollinearity, which…

协变量依赖删失下半竞争风险数据的双重稳健估计AI 解读

Semi-competing risks occur when individuals may experience a non-terminal event and a terminal event, where the terminal event censors the non-terminal event but not vice versa. In the presence of covariate-dependent censoring, augmented inverse probability of censoring weighting (AIPCW) framework has not been developed for the special setting of semi-competing risks which, unlike competing risks, have asymmetric event time structure and complex estimands. Using…

目标试验模拟中有效统计推断的样本分割方法AI 解读

\noindent Target trial emulation has improved comparative effectiveness research by making the causal question, assumptions, and analysis plan explicit. However, target trial protocols are usually developed iteratively. That is, after examining the data, investigators revise the protocol to reflect which target trials the observational data can realistically support. This iterative procedure is part of normal scientific practice, but it raises concerns about selective…

函数单调性的多重检验AI 解读

Instead of having a single "yes" or "no" result from a test of the global null hypothesis that a function is increasing, we propose a multiple testing procedure of the function's increasingness at several points. If the global null is rejected, then multiple testing provides more information about why. If the global null is not rejected, then multiple testing can provide stronger evidence in favor of increasingness, by rejecting null hypotheses that the function is…

基于负二项边际与高斯 Copula 的重复计数结局参照组敏感性分析AI 解读

Reference-based multiple imputation is used in longitudinal clinical trials to assess sensitivity to assumptions about outcomes unobserved after intercurrent events. Most existing methods target continuous outcomes and use multivariate normal working models. Scheduled count outcomes require a model that preserves integer support, skewness, overdispersion, longitudinal dependence, and an exposure-based rate interpretation. We propose visit-specific negative binomial (NB)…

方向性分布偏移下的实验地点选择AI 解读

Multi-site studies are widely used across the social sciences, education, and medicine to learn the effects of interventions across heterogeneous populations. But deployment populations, on which a given intervention is implemented, may differ systematically from the experimental population on which the intervention is evaluated. I propose a method for selecting sites with minimax guarantees against directional distribution shifts, which allows the researcher to guard…

析因多变量贝叶斯因果森林：多处理的异质主效应与交互效应AI 解读

Many studies expose units to several binary treatments at once and record several correlated outcomes, yet analysts usually estimate one treatment's average effect on one outcome at a time -- discarding how treatments interact and how their effects vary across units. We introduce the factorial multivariate Bayesian causal forest, a Bayesian nonparametric model that decomposes the factorial response surface over the treatment lattice into a prognostic sum-of-trees plus one…

弱代理变量下近端因果学习的同时推断AI 解读

Proximal causal learning uses the treatment-inducing proxy and the outcome-inducing proxy through the outcome bridge and the treatment bridge to provide a way to address unobserved confounding in observational studies. This paper studies simultaneous inference for the marginal counterfactual distribution and quantile treatment effect under weak proxies. First, we propose a measure of weak-proxy strength and tightens the population-bias bound for the counterfactual CDF…